






"""simple docstring"""





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowerCamelCase							=  logging.get_logger(__name__)

# General docstring
__lowerCamelCase							=  'RegNetConfig'

# Base docstring
__lowerCamelCase							=  'facebook/regnet-y-040'
__lowerCamelCase							=  [1, 10_88, 7, 7]

# Image classification docstring
__lowerCamelCase							=  'facebook/regnet-y-040'
__lowerCamelCase							=  'tabby, tabby cat'

__lowerCamelCase							=  [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      int     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 1     ,		__snake_case  :      int = 1     ,		__snake_case  :      Optional[str] = "relu"     ,		**__snake_case  :      str     ,		)			->							Any:
											super().__init__(**__snake_case					)
											# The padding and conv has been verified in
											# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
											__magic_name__:	Optional[int]							=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2					)
											__magic_name__:	Dict							=       tf.keras.layers.ConvaD(
											    filters=__snake_case     ,		kernel_size=__snake_case     ,		strides=__snake_case     ,		padding="""VALID"""     ,		groups=__snake_case     ,		use_bias=__snake_case     ,		name="""convolution"""     ,		)
											__magic_name__:	int							=       tf.keras.layers.BatchNormalization(epsilon=1E-5     ,		momentum=0.9     ,		name="""normalization"""					)
											__magic_name__:	Optional[int]							=       ACTaFN[activation] if activation is not None else tf.identity






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      str					)			->							Dict:
											__magic_name__:	Optional[Any]							=       self.convolution(self.padding(__snake_case					)					)
											__magic_name__:	Union[str, Any]							=       self.normalization(__snake_case					)
											__magic_name__:	Tuple							=       self.activation(__snake_case					)
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		**__snake_case  :      Dict					)			->							Optional[int]:
											super().__init__(**__snake_case					)
											__magic_name__:	Tuple							=       config.num_channels
											__magic_name__:	Optional[int]							=       TFRegNetConvLayer(
											    out_channels=config.embedding_size     ,		kernel_size=3     ,		stride=2     ,		activation=config.hidden_act     ,		name="""embedder"""     ,		)






					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Dict					)			->							int:
											__magic_name__:	Union[str, Any]							=       shape_list(__snake_case					)[1]
											if tf.executing_eagerly() and num_channels != self.num_channels:
																	raise ValueError(
																	    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""					)

											# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
											# So change the input format from `NCHW` to `NHWC`.
											# shape = (batch_size, in_height, in_width, in_channels=num_channels)
											__magic_name__:	Any							=       tf.transpose(__snake_case     ,		perm=(0, 2, 3, 1)					)
											__magic_name__:	Dict							=       self.embedder(__snake_case					)
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int = 2     ,		**__snake_case  :      Any					)			->							Dict:
											super().__init__(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       tf.keras.layers.ConvaD(
											    filters=__snake_case     ,		kernel_size=1     ,		strides=__snake_case     ,		use_bias=__snake_case     ,		name="""convolution"""					)
											__magic_name__:	Dict							=       tf.keras.layers.BatchNormalization(epsilon=1E-5     ,		momentum=0.9     ,		name="""normalization"""					)






					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      bool = False					)			->							tf.Tensor:
											return self.normalization(self.convolution(__snake_case					)     ,		training=__snake_case					)



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		**__snake_case  :      str					)			->							str:
											super().__init__(**__snake_case					)
											__magic_name__:	Optional[Any]							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case     ,		name="""pooler"""					)
											__magic_name__:	Optional[Any]							=       [
											    tf.keras.layers.ConvaD(filters=__snake_case     ,		kernel_size=1     ,		activation="""relu"""     ,		name="""attention.0"""					),
											    tf.keras.layers.ConvaD(filters=__snake_case     ,		kernel_size=1     ,		activation="""sigmoid"""     ,		name="""attention.2"""					),
											]






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[str]					)			->							List[Any]:
											# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
											__magic_name__:	List[str]							=       self.pooler(__snake_case					)
											for layer_module in self.attention:
																	__magic_name__:	List[str]							=       layer_module(__snake_case					)
											__magic_name__:	Optional[Any]							=       hidden_state * pooled
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int = 1     ,		**__snake_case  :      Optional[int]					)			->							Optional[int]:
											super().__init__(**__snake_case					)
											__magic_name__:	List[str]							=       in_channels != out_channels or stride != 1
											__magic_name__:	Union[str, Any]							=       max(1     ,		out_channels // config.groups_width					)
											__magic_name__:	Optional[Any]							=       (
											    TFRegNetShortCut(__snake_case     ,		stride=__snake_case     ,		name="""shortcut"""					)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation("""linear"""     ,		name="""shortcut"""					)
											)
											# `self.layers` instead of `self.layer` because that is a reserved argument.
											__magic_name__:	List[str]							=       [
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=config.hidden_act     ,		name="""layer.0"""					),
											    TFRegNetConvLayer(
											        __snake_case     ,		stride=__snake_case     ,		groups=__snake_case     ,		activation=config.hidden_act     ,		name="""layer.1"""					),
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=__snake_case     ,		name="""layer.2"""					),
											]
											__magic_name__:	Any							=       ACTaFN[config.hidden_act]






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Any					)			->							Union[str, Any]:
											__magic_name__:	Any							=       hidden_state
											for layer_module in self.layers:
																	__magic_name__:	Optional[int]							=       layer_module(__snake_case					)
											__magic_name__:	str							=       self.shortcut(__snake_case					)
											hidden_state += residual
											__magic_name__:	int							=       self.activation(__snake_case					)
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      RegNetConfig     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int = 1     ,		**__snake_case  :      Union[str, Any]					)			->							Dict:
											super().__init__(**__snake_case					)
											__magic_name__:	str							=       in_channels != out_channels or stride != 1
											__magic_name__:	Dict							=       max(1     ,		out_channels // config.groups_width					)
											__magic_name__:	Tuple							=       (
											    TFRegNetShortCut(__snake_case     ,		stride=__snake_case     ,		name="""shortcut"""					)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation("""linear"""     ,		name="""shortcut"""					)
											)
											__magic_name__:	str							=       [
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=config.hidden_act     ,		name="""layer.0"""					),
											    TFRegNetConvLayer(
											        __snake_case     ,		stride=__snake_case     ,		groups=__snake_case     ,		activation=config.hidden_act     ,		name="""layer.1"""					),
											    TFRegNetSELayer(__snake_case     ,		reduced_channels=int(round(in_channels / 4					)					)     ,		name="""layer.2"""					),
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=__snake_case     ,		name="""layer.3"""					),
											]
											__magic_name__:	Optional[int]							=       ACTaFN[config.hidden_act]






					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      int					)			->							Dict:
											__magic_name__:	int							=       hidden_state
											for layer_module in self.layers:
																	__magic_name__:	Optional[Any]							=       layer_module(__snake_case					)
											__magic_name__:	Union[str, Any]							=       self.shortcut(__snake_case					)
											hidden_state += residual
											__magic_name__:	Any							=       self.activation(__snake_case					)
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      int     ,		__snake_case  :      RegNetConfig     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int = 2     ,		__snake_case  :      int = 2     ,		**__snake_case  :      List[Any]					)			->							Optional[int]:
											super().__init__(**__snake_case					)

											__magic_name__:	int							=       TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
											__magic_name__:	Optional[Any]							=       [
											    # downsampling is done in the first layer with stride of 2
											    layer(__snake_case     ,		__snake_case     ,		__snake_case     ,		stride=__snake_case     ,		name="""layers.0"""					),
											    *[layer(__snake_case     ,		__snake_case     ,		__snake_case     ,		name=F'layers.{i+1}'					) for i in range(depth - 1					)],
											]






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Union[str, Any]					)			->							Tuple:
											for layer_module in self.layers:
																	__magic_name__:	Dict							=       layer_module(__snake_case					)
											return hidden_state



class       __A		(   tf.keras.layers.Layer			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		**__snake_case  :      Optional[Any]					)			->							Dict:
											super().__init__(**__snake_case					)
											__magic_name__:	List[Any]							=       []
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    TFRegNetStage(
											        __snake_case     ,		config.embedding_size     ,		config.hidden_sizes[0]     ,		stride=2 if config.downsample_in_first_stage else 1     ,		depth=config.depths[0]     ,		name="""stages.0"""     ,		)					)
											__magic_name__:	Dict							=       zip(config.hidden_sizes     ,		config.hidden_sizes[1:]					)
											for i, ((in_channels, out_channels), depth) in enumerate(zip(__snake_case     ,		config.depths[1:]					)					):
																	self.stages.append(TFRegNetStage(__snake_case     ,		__snake_case     ,		__snake_case     ,		depth=__snake_case     ,		name=F'stages.{i+1}'					)					)






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = True					)			->							TFBaseModelOutputWithNoAttention:
											__magic_name__:	int							=       () if output_hidden_states else None

											for stage_module in self.stages:
																	if output_hidden_states:
																							__magic_name__:	Optional[Any]							=       hidden_states + (hidden_state,)

																	__magic_name__:	Optional[Any]							=       stage_module(__snake_case					)

											if output_hidden_states:
																	__magic_name__:	int							=       hidden_states + (hidden_state,)

											if not return_dict:
																	return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

											return TFBaseModelOutputWithNoAttention(last_hidden_state=__snake_case     ,		hidden_states=__snake_case					)



@keras_serializable
class       __A		(   tf.keras.layers.Layer			):
					UpperCAmelCase__								=				RegNetConfig

					def __init__(			self  :      Optional[int]     ,		__snake_case  :      Any     ,		**__snake_case  :      List[str]					)			->							int:
											super().__init__(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       config
											__magic_name__:	Optional[int]							=       TFRegNetEmbeddings(__snake_case     ,		name="""embedder"""					)
											__magic_name__:	int							=       TFRegNetEncoder(__snake_case     ,		name="""encoder"""					)
											__magic_name__:	int							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case     ,		name="""pooler"""					)






					@unpack_inputs
					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      bool = False     ,		)			->							TFBaseModelOutputWithPoolingAndNoAttention:
											__magic_name__:	Optional[int]							=       (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__magic_name__:	int							=       return_dict if return_dict is not None else self.config.use_return_dict

											__magic_name__:	List[str]							=       self.embedder(__snake_case     ,		training=__snake_case					)

											__magic_name__:	Optional[Any]							=       self.encoder(
											    __snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case					)

											__magic_name__:	str							=       encoder_outputs[0]
											__magic_name__:	List[Any]							=       self.pooler(__snake_case					)

											# Change to NCHW output format have uniformity in the modules
											__magic_name__:	int							=       tf.transpose(__snake_case     ,		perm=(0, 3, 1, 2)					)
											__magic_name__:	List[str]							=       tf.transpose(__snake_case     ,		perm=(0, 3, 1, 2)					)

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																	__magic_name__:	List[str]							=       tuple([tf.transpose(__snake_case     ,		perm=(0, 3, 1, 2)					) for h in encoder_outputs[1]]					)

											if not return_dict:
																	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=__snake_case     ,		pooler_output=__snake_case     ,		hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states     ,		)



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				RegNetConfig
					UpperCAmelCase__								=				"regnet"
					UpperCAmelCase__								=				"pixel_values"

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Union[str, Any]:
											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)     ,		dtype=tf.floataa					)}


__lowerCamelCase							=  r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

__lowerCamelCase							=  r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."     ,SCREAMING_SNAKE_CASE_     ,)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      RegNetConfig     ,		*__snake_case  :      List[Any]     ,		**__snake_case  :      Tuple					)			->							Tuple:
											super().__init__(__snake_case     ,		*__snake_case     ,		**__snake_case					)
											__magic_name__:	List[str]							=       TFRegNetMainLayer(__snake_case     ,		name="""regnet"""					)






					@unpack_inputs
					@add_start_docstrings_to_model_forward(__snake_case					)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC     ,		output_type=__snake_case     ,		config_class=_CONFIG_FOR_DOC     ,		modality="""vision"""     ,		expected_output=_EXPECTED_OUTPUT_SHAPE     ,		)
					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      int=False     ,		)			->							Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
											__magic_name__:	Optional[int]							=       (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__magic_name__:	Union[str, Any]							=       return_dict if return_dict is not None else self.config.use_return_dict

											__magic_name__:	List[str]							=       self.regnet(
											    pixel_values=__snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case     ,		)
											if not return_dict:
																	return (outputs[0],) + outputs[1:]

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=outputs.last_hidden_state     ,		pooler_output=outputs.pooler_output     ,		hidden_states=outputs.hidden_states     ,		)



@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "     ,SCREAMING_SNAKE_CASE_     ,)
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      int     ,		__snake_case  :      RegNetConfig     ,		*__snake_case  :      Any     ,		**__snake_case  :      Any					)			->							Optional[Any]:
											super().__init__(__snake_case     ,		*__snake_case     ,		**__snake_case					)
											__magic_name__:	Union[str, Any]							=       config.num_labels
											__magic_name__:	Tuple							=       TFRegNetMainLayer(__snake_case     ,		name="""regnet"""					)
											# classification head
											__magic_name__:	List[Any]							=       [
											    tf.keras.layers.Flatten(),
											    tf.keras.layers.Dense(config.num_labels     ,		name="""classifier.1"""					) if config.num_labels > 0 else tf.identity,
											]






					@unpack_inputs
					@add_start_docstrings_to_model_forward(__snake_case					)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,		output_type=__snake_case     ,		config_class=_CONFIG_FOR_DOC     ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,		)
					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      tf.Tensor = None     ,		__snake_case  :      tf.Tensor = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict=False     ,		)			->							Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
											__magic_name__:	Optional[Any]							=       (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__magic_name__:	Optional[Any]							=       return_dict if return_dict is not None else self.config.use_return_dict

											__magic_name__:	Any							=       self.regnet(
											    __snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case					)

											__magic_name__:	Optional[Any]							=       outputs.pooler_output if return_dict else outputs[1]

											__magic_name__:	Optional[int]							=       self.classifier[0](__snake_case					)
											__magic_name__:	List[Any]							=       self.classifier[1](__snake_case					)

											__magic_name__:	Optional[int]							=       None if labels is None else self.hf_compute_loss(labels=__snake_case     ,		logits=__snake_case					)

											if not return_dict:
																	__magic_name__:	List[Any]							=       (logits,) + outputs[2:]
																	return ((loss,) + output) if loss is not None else output

											return TFSequenceClassifierOutput(loss=__snake_case     ,		logits=__snake_case     ,		hidden_states=outputs.hidden_states					)








'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase:   Dict					=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:   Union[str, Any]					=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:   Union[str, Any]					=		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:   Optional[Any]					=		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:   int					=		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase:   List[Any]					=		[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def  lowerCamelCase__			(		_A							):
  a		:   Optional[Any]	  =	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,     _A							)
  return [m.group(0							) for m in matches]
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:   Union[str, Any]	  =	{
      config.replace('Config'					,     ''							): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  a		:   Union[str, Any]	  =	collections.defaultdict(_A							)
  a		:   Optional[Any]	  =	collections.defaultdict(_A							)
  a		:   int	  =	collections.defaultdict(_A							)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_A							):
    a		:   Optional[Any]	  =	None
    if _re_tf_models.match(_A							) is not None:
      a		:   List[str]	  =	tf_models
      a		:   str	  =	_re_tf_models.match(_A							).groups()[0]
    elif _re_flax_models.match(_A							) is not None:
      a		:   Optional[int]	  =	flax_models
      a		:   List[Any]	  =	_re_flax_models.match(_A							).groups()[0]
    elif _re_pt_models.match(_A							) is not None:
      a		:   int	  =	pt_models
      a		:   List[str]	  =	_re_pt_models.match(_A							).groups()[0]

    if lookup_dict is not None:
      while len(_A							) > 0:
        if attr_name in model_prefix_to_model_type:
          a		:   Optional[Any]	  =	True
          break
        # Try again after removing the last word in the name
        a		:   List[str]	  =	''.join(camel_case_split(_A							)[:-1]							)

  a		:   Any	  =	set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
  a		:   int	  =	list(_A							)
  all_models.sort()

  a		:   Optional[Any]	  =	{'model_type': all_models}
  a		:   List[str]	  =	[pt_models[t] for t in all_models]
  a		:   str	  =	[tf_models[t] for t in all_models]
  a		:   int	  =	[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  a		:   Any	  =	{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      a		:   Optional[int]	  =	'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      a		:   Union[str, Any]	  =	'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      a		:   Tuple	  =	'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      a		:   str	  =	'AutoTokenizer'

  a		:   str	  =	[processors[t] for t in all_models]

  return pd.DataFrame(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    a		:   Optional[Any]	  =	[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    a		:   Optional[int]	  =	[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_A					,     _A					,     _A							):
      # The type of pipeline may not exist in this framework
      if not hasattr(_A					,     _A							):
        continue
      # First extract all model_names
      a		:   List[str]	  =	[]
      for name in getattr(_A					,     _A							).values():
        if isinstance(_A					,     _A							):
          model_names.append(_A							)
        else:
          model_names.extend(list(_A							)							)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

  return table
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	get_frameworks_table()
  a		:   List[str]	  =	Dataset.from_pandas(_A							)

  a		:   Optional[Any]	  =	hf_hub_download(
      'huggingface/transformers-metadata'					,     'pipeline_tags.json'					,     repo_type='dataset'					,     token=_A							)
  a		:   List[Any]	  =	Dataset.from_json(_A							)
  a		:   Optional[Any]	  =	{
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_A							)							)
  }
  a		:   Dict	  =	update_pipeline_and_auto_class_table(_A							)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  a		:   str	  =	sorted(table.keys()							)
  a		:   List[Any]	  =	pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }							)
  a		:   Optional[Any]	  =	Dataset.from_pandas(_A							)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_A					,     'frameworks.json'							)							)
    tags_dataset.to_json(os.path.join(_A					,     'pipeline_tags.json'							)							)

    if commit_sha is not None:
      a		:   int	  =	(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      a		:   Optional[int]	  =	'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'					,     folder_path=_A					,     repo_type='dataset'					,     token=_A					,     commit_message=_A					,     )
def  lowerCamelCase__			(		):
  a		:   int	  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  a		:   List[Any]	  =	transformers_module.pipelines.SUPPORTED_TASKS
  a		:   str	  =	[]
  for key in pipeline_tasks:
    if key not in in_table:
      a		:   Dict	  =	pipeline_tasks[key]['pt']
      if isinstance(_A					,     (list, tuple)							):
        a		:   List[Any]	  =	model[0]
      a		:   Union[str, Any]	  =	model.__name__
      if model not in in_table.values():
        missing.append(_A							)

  if len(_A							) > 0:
    a		:   List[str]	  =	', '.join(_A							)
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""							)


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		argparse.ArgumentParser()
     parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
     parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
     parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()

     if args.check_only:
          check_pipeline_tags()
     else:
          update_metadata(args.token, args.commit_sha)